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Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data

Research output: Contribution to journalArticlepeer-review

Caroline F Wright, Tomas W Fitzgerald, Wendy D Jones, Stephen Clayton, Jeremy F McRae, Margriet van Kogelenberg, Daniel A King, Kirsty Ambridge, Daniel M Barrett, Tanya Bayzetinova, A Paul Bevan, Eugene Bragin, Eleni A Chatzimichali, Susan Gribble, Philip Jones, Netravathi Krishnappa, Laura E Mason, Ray Miller, Katherine I Morley, Vijaya Parthiban & 13 more Elena Prigmore, Diana Rajan, Alejandro Sifrim, G Jawahar Swaminathan, Adrian R Tivey, Anna Middleton, Michael Parker, Nigel P Carter, Jeffrey C Barrett, Matthew E Hurles, David R FitzPatrick, Helen V Firth, on behalf of the DDD study

Original languageEnglish
Pages (from-to)1305-1314
Number of pages10
JournalLancet
Volume385
Issue number9975
Early online date17 Dec 2014
DOIs
E-pub ahead of print17 Dec 2014
Published4 Apr 2015

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    1_s2.0_S0140673614617050_main.pdf, 2.46 MB, application/pdf

    Uploaded date:21 Jul 2015

    Version:Final published version

    Licence:CC BY

    Copyright © 2014 Wright et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.

King's Authors

  • on behalf of the DDD study

Abstract

BACKGROUND: Human genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount.

METHODS: The Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team.

FINDINGS: Around 80 000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation.

INTERPRETATION: Implementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene-phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial.

FUNDING: Health Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health.

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